Attach packages

library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)

Monthly US energy consumption (renewables)

us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>% 
  clean_names()
renew_clean <- us_renew %>% 
  mutate(description = str_to_lower(description)) %>% 
  filter(str_detect(description, pattern = "consumption")) %>% 
  filter(!str_detect(description, pattern = "total"))

Convert ‘yyyymm’ column to a date

renew_date <- renew_clean %>% 
  mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>% 
  mutate(month_sep = yearmonth(yr_mo_day)) %>% 
  mutate(value = as.numeric(value)) %>% 
  drop_na(month_sep, value)

# Make a version where I have month and year in separate columns

renew_parsed <- renew_date %>% 
  mutate(month = month(yr_mo_day, label = TRUE)) %>% 
  mutate(year = year(yr_mo_day))

Look at it:

renew_gg <- ggplot(data = renew_date,
                   aes(x = month_sep, 
                       y = value, 
                       group = description)) +
  geom_line(aes(color = description))

renew_gg

Updating my colors with paletteer palettes:

renew_gg +
  scale_color_paletteer_d("DresdenColor::bloodrites")

Coerce our renew_parsed to a tsibble (time-series enabled data frame)

renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)

Let’s look at our ts data in a couple different ways:

renew_ts %>% autoplot(value)

# this works because the key was already specified as description above

renew_ts %>% gg_subseries(value)

# renew_ts %>% gg_season(value) - doesn't always work

ggplot(data = renew_parsed, 
       aes(x = month, 
           y = value, 
           group = year)) +
  geom_line(aes(color = year)) +
  facet_wrap(~description, 
             ncol = 1, 
             scales = "free", 
             strip.position = "right")

Just look at the hydroelectric energy consumption

hydro_ts <- renew_ts %>% 
  filter(description == "hydroelectric power consumption")

hydro_ts %>% autoplot(value)

hydro_ts %>% gg_subseries(value)

ggplot(hydro_ts, aes(x = month, y = value, group = year)) +
  geom_line(aes(color = year))

What if I want the quarterly average consumption for hydro?

hydro_quarterly <- hydro_ts %>% 
  index_by(year_qu = ~(yearquarter(.))) %>% 
  summarize(avg_consumption = mean(value))

head(hydro_quarterly)
## # A tsibble: 6 x 2 [1Q]
##   year_qu avg_consumption
##     <qtr>           <dbl>
## 1 1973 Q1            261.
## 2 1973 Q2            255.
## 3 1973 Q3            212.
## 4 1973 Q4            225.
## 5 1974 Q1            292.
## 6 1974 Q2            290.

Decompose hydro_ts

dcmp <- hydro_ts %>% 
  model(STL(value ~ season(window = 5)))

# window specifies moving avg window
# value as a function of season

components(dcmp) %>% autoplot()

# residual component is usually less than 10% of total range of values (e.g. here it ranges from about 150 - 300 trillion BTUs, errors about 20)